Article(id=1281608067531911764, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.04.0702, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1763049600000, receivedDateStr=2025-11-14, revisedDate=1770134400000, revisedDateStr=2026-02-04, acceptedDate=null, acceptedDateStr=null, onlineDate=1783488774425, onlineDateStr=2026-07-08, pubDate=1777305600000, pubDateStr=2026-04-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783488774425, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783488774425, creator=13701087609, updateTime=1783488774425, updator=13701087609, issue=Issue{id=1281608054340829934, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='4', pageStart='1', pageEnd='286', issueExtLink='null', onlineDate='null', pubDate='1777305600000', pubDateStr='2026-04-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783488771281, creator='13701087609', updateTime=1783588648031, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282026967885124001, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282026967885124002, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=75, endPage=84, ext={EN=ArticleExt(id=1281608067733238357, articleId=1281608067531911764, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Control and cause analysis for high-risk operations combined knowledge graph and Apriori algorithm, columnId=1277328337617941059, journalTitle=China Safety Science Journal, columnName=Safety Technology and Engineering, runingTitle=null, highlight=null, articleAbstract=

In order to improve the safety management level of high-risk operations, an intelligent management and control method was proposed based on the knowledge graph and the Apriori algorithm. First, some high-risk operation accidents were collected. Then, the accident causes were extracted and classified automatically by Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory-Conditional Random Field (BERT-BiLSTM-CRF) model, which constructed a knowledge graph of high-risk operation accidents, storing relevant accident data. Subsequently, an index system of high-risk operation causative factors was established, combined with the corresponding operation standard specification. Furthermore, the correlation among causative factors was determined by the Apriori algorithm. Finally, some countermeasures were proposed for targeted control. In this paper, the application of confined space operations was used to demonstrate this method. The results showed that the main causes of confined space accidents are not wearing safety protective equipment", blind rescue", not being equipped with gas detection and other equipment", missing safety warning signs", insufficient safety education", and inadequate safety management". Moreover, the correlation between insufficient safety education" and not wearing safety protective equipment" is greater (80.7% support, 60.41% confidence), and the relationship between insufficient safety education" and blind rescue" also has a strong association (80.7% support, 55.88% confidence).

, authors=Hanjun Guo¹, Huaying Cui², Zhenqi Hu², Rongxue Kang³, Jinlong Zhao^{**, 2}, authorsList=Hanjun Guo, Huaying Cui, Zhenqi Hu, Rongxue Kang, Jinlong Zhao, authorCompany=null, correspAuthors=Jinlong Zhao, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1281608068047811158, articleId=1281608067531911764, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=融合知识图谱和Apriori算法的高风险作业致因分析与管控, columnId=1277328337940902469, journalTitle=中国安全科学学报, columnName=安全技术与工程, runingTitle=null, highlight=null, articleAbstract=

为提高高风险作业安全管理水平，提出一种基于知识图谱和Apriori算法的高风险作业智能精准管控方法。首先，搜集高风险作业相关事故案例，利用基于变换器的双向编码器表征-双向长短期记忆网络-条件随机场（BERT-BiLSTM-CRF）模型实现事故原因自动分类抽取，并构建高风险作业知识图谱，存储相关事故数据；然后，结合作业标准规范构建高风险作业致因要素指标体系，并利用Apriori算法明确高风险作业致因要素之间的关联关系；最后，提出针对性管控措施，以有限空间作业为例开展具体阐述。结果表明：作业人员未佩戴安全防护装备、盲目施救、未配备气体检测等设备、安全警示标志缺失、安全教育培训不到位和安全管理不到位是导致有限空间作业事故发生的主要原因。安全教育不到位与未佩戴安全防护装备的关联度较大，支持度为80.7%，置信度为60.41%；安全教育培训不到位与盲目施救的关联性也较强，支持度为80.7%，置信度为55.88%。

, authors=国汉君研究员¹, 崔华莹², 胡振启², 康荣学研究员³, 赵金龙教授^{**, 2}, authorsList=国汉君研究员, 崔华莹, 胡振启, 康荣学研究员, 赵金龙教授, authorCompany=null, correspAuthors=赵金龙教授, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=CEq1tYi+lYbgo5kKpWz/0w==, magXml=JbiI+4RVO99nW3Qz0OU4vQ==, pdfUrl=null, pdf=UT1luuGia0yj5EKOiyHb0w==, pdfFileSize=2208775, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=H33bFgB1RRslF3EpOd0jQw==, mapNumber=null, fund=null)}, authors=[Author(id=1281608068479824481, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608067531911764, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=hanjun.guo@chnenergy.com.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281608068542739043, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608067531911764, authorId=1281608068479824481, language=EN, stringName=Hanjun Guo, firstName=Hanjun, middleName=null, lastName=Guo, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹China Energy Investment Corporation Limited, Beijing 100011, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281608068609847908, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608067531911764, authorId=1281608068479824481, language=CN, stringName=国汉君研究员, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹国家能源集团，北京 100011, bio={"content":"

国汉君（1965—），男，内蒙古呼伦贝尔人，硕士，研究员，主要从事企业安全生产与应急救援等方面的工作。E-mail: hanjun.guo@chnenergy.com.cn。

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国汉君（1965—），男，内蒙古呼伦贝尔人，硕士，研究员，主要从事企业安全生产与应急救援等方面的工作。E-mail: hanjun.guo@chnenergy.com.cn。

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